LangTweet
This is an example repository demonstrating the power of dynamic few-shot prompting. We use LangSmith to serve a few-shot dataset that we use to prompt an LLM to generate tweets in a style we like. We use LangGraph to orchestrate the (relatively simple) graph.
Key Links:
The graph
The graph logic is very simple for now.
First, we load the content of a given url.
You can find the logic for loading the content in langtweet/loading.py.
After that, we pass the content to a prompt.
This prompt contains some basic instructions, but more importantly a few examples of similar tweets in the past.
The logic for this can be found in langtweet/agent.py
The dynamic few-shot selection
A key part of this application is using dynamic few-shot selection to help prompt. The prompt instructions for tweeting are pretty basic. It provides a bit of context then tells the LLM to pay attention to the examples. Therefor, the examples are doing a lot of lifting here.
We use LangSmith to manage and serve the dataset that we use as examples.
The deployment
We deploy the graph to LangGraph Cloud. This gives us a nice API to interact with, as well as fun studio UI to use to try out the graph.
We've made this studio publicly accessible, you can use it here.
The feedback loop
A key part of a system like this is a feedback loop to continue to gather examples that can be used in the future. To that end, we have provided a very simple Streamlit application for invoking the graph and then correcting it and giving feedback. This feedback automatically creates a new entry in the dataset, which can then be used in the future.